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The visual analysis of spatial regression.

Record Type:	Electronic resources : Monograph/item
Title/Author:	The visual analysis of spatial regression.
Author:	Pederson, Thomas William.
Description:	123 p.
Notes:	Adviser: Charles Dana Tomlin.
Notes:	Source: Dissertation Abstracts International, Volume: 61-03, Section: A, page: 1192.
Contained By:	Dissertation Abstracts International61-03A.
Subject:	Urban and Regional Planning.
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=9965540
ISBN:	0599701552

The visual analysis of spatial regression.
Pederson, Thomas William.

The visual analysis of spatial regression. [electronic resource] - 123 p.

Adviser: Charles Dana Tomlin.

Thesis (Ph.D.)--University of Pennsylvania, 2000.

The primary objective of this research is to develop a set of digital visualization techniques with which to examine residual autocorrelation in spatial regression models using a spatial weight matrix. Its secondary objective is to explore and refine the process by which these techniques are developed as a prototype for the more general design of user-centered, task-driven components in a geographic information system. The research performed visual experiments on the results of spatial regression models that informed the design of an prototypical tool set developed for a specific category of spatial statistical analysis. The approach is presented as a paradigm for the design of other such tools. It includes user-centered, task-driven components that consists of a collection of spatial statistical and visualization techniques developed to facilitate the examination of residual autocorrelation using a spatial weight matrix. The research resulted in an interface design that combines ease of use, intuition, and the appeal of visualization with the relative certainty of statistics.

ISBN: 0599701552Subjects--Topical Terms:

212416
Urban and Regional Planning.

The visual analysis of spatial regression.
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520 # $a The primary objective of this research is to develop a set of digital visualization techniques with which to examine residual autocorrelation in spatial regression models using a spatial weight matrix. Its secondary objective is to explore and refine the process by which these techniques are developed as a prototype for the more general design of user-centered, task-driven components in a geographic information system. The research performed visual experiments on the results of spatial regression models that informed the design of an prototypical tool set developed for a specific category of spatial statistical analysis. The approach is presented as a paradigm for the design of other such tools. It includes user-centered, task-driven components that consists of a collection of spatial statistical and visualization techniques developed to facilitate the examination of residual autocorrelation using a spatial weight matrix. The research resulted in an interface design that combines ease of use, intuition, and the appeal of visualization with the relative certainty of statistics.
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